The transportation arts, transportation system arts, the public transit infrastructure arts, the data processing arts, the data analysis arts, the transportation modeling arts, the predictive arts, and the like.
Improving the transportation of urban areas is a constant challenge for any transportation authority. This challenge is becoming even larger within the fast growing mega cities over the world. These fast growing cities will have to solve the issues of traffic congestion and pollution if they want to stay attractive and continue their economic growth. Planning new infrastructure, in particular for public transit, is one important dimension where transportation authorities can act. However development of infrastructure is costly, time consuming, and heavy work for a city. Past such development required therefore a very careful study of the mobility needs and simulation of different alternatives before taking such decision.
Currently, the planning of a new transportation infrastructure involves simulation of several options based on an understanding of the city. This understanding of the city is primarily based upon a custom study. This custom study generally consists of a period of m collection of data and traveler/citizen surveys, which are then aggregated into a model of the city by some expert analysts. This approach is very time consuming and expensive, and therefore, it often has the disadvantages to rely only on very partial data (only few thousands of people surveyed) or non-up-to-date data (e.g., global census of 5 years ago). Furthermore aggregating this data into a model is such an enormous work that data are often aggregated to a high level of granularity, which allows only a macro simulation of the city mobility dynamics. The cost/precision ratio of simulations built from these custom studies is therefore not optimal.
The fielding of more and more intelligent transportation systems allows the collection of millions of transactions which constitute a very detailed source of information. This is for example, public transportation ticketing data that are collected from automatic fare collection systems each time a passenger checks-in (and sometimes checks-out) before boarding (alighting) a vehicle. Currently, this collected ticketing data has been very limitedly used for understanding mobility patterns of a city. What is needed is a mechanism that will use this data in order to learn automatically city models that can then be used for simulation and planning of new infrastructure, as well as increase significantly the planning cost/precision ratio through the integration of a much more massive, more up to date set of information, in an automated manner.
Thus, it would be advantageous to provide an effective system and method for learning transportation models of an associated transportation system, including demand models estimating the geographical location in the city of a traveler's actual origin and destination to facilitate infrastructure planning.